发表机构
Applied Robotics and AI Solutions (ARAS); Faculties of Electrical and Computer Engineering; K. N. Toosi University of Technology（应用机器人与人工智能解决方案（ARAS）; 电气与计算机工程学院; K. N. 图西理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建了结合ARAS-Farabi数据集与深度卷积神经网络的平台，可辅助AI智能体生成改进手术路径，使受训外科医生运动路径提升至少20%并保留其意图。
AI 中文摘要
外科医生的自动化培训是显著降低外科培训风险和成本的最关键因素之一。随着人工智能（AI）知识的最新进展以及各类手术可用数据的增加，AI在外科培训中的参与正变得极具前景。建议在AI发展的早期阶段，它作为第三方与培训师一同介入手术。随着对AI的信任度提升，这一过程将在未来使AI智能体充当培训师。AI可介入培训过程的第一阶段是向培训师建议改进的手术路径。为完成此任务并提升受训外科医生的运动路径，第一步必须构建一个平台。本文介绍了该平台以及名为ARAS-Farabi的带注释撕囊术手术数据集。本研究中，使用JIGSAWS和ARAS-Farabi手术数据集对深度卷积神经网络进行预训练，该网络可从手术器械尖端运动数据中提取手术技能特征。所提出的平台从专家外科医生运动轨迹的特征空间中开发参考模型，并提出改进路径以提升新手外科医生的技能。研究采用包含两个损失函数的优化方法，生成既能提升新手外科医生路径技能水平，又能预测并保留其意图的路径。研究结果表明，在AI智能体的辅助下，受训外科医生的运动路径可在保持其意图目标的同时提升至少20%。除推荐的深度网络外，本研究还开发了各类可量化指标以验证受训者的提升水平。
英文摘要
Automated training of surgeons is one of the most crucial factors that significantly minimize surgical training risks and expenses. With recent advances in artificial intelligence (AI) knowledge and available data from various surgeries, AI's involvement in surgical training is becoming very promising. It is recommended that at the early stages of AI development, it interferes in the surgery as a third agent alongside the trainer. As trust in AI increases, this process will lead to an AI agent acting as a trainer in the future. The first phase in which AI can intervene in the training process is to suggest an improved surgical path to the trainer. A platform must be constructed in the first step, to accomplish this task and to enhance the movement path of trainee surgeons. This paper introduces this platform along with an annotated capsulorhexis surgery dataset called the ARAS-Farabi dataset. In this research, a deep convolutional neural network is pre-trained with JIGSAWS and ARAS-Farabi surgical datasets that can extract surgical skill characteristics from surgery tool tip motion data. The proposed platform develops a reference model from the feature space of an expert surgeon's movement trajectory and proposes an improved path to enhance the skill of a novice surgeon. An optimization with two loss functions is utilized to create a path that raises the skill level of the novice surgeon's path while simultaneously predicting and preserving his/her intent. The results of this study reveal that, with the assistance of an AI agent, the trainee surgeon's movement path can be enhanced by at least 20 percent while maintaining his intentional objective. In addition to the recommended deep network, various tangible indicators have also been developed in this research to verify the level of trainee improvement.
